{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据分析中，散点图是最常用得探索两个数据之间相关关系得可视化图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "N = 1000\n",
    "x = np.random.randn(N)\n",
    "y = np.random.randn(len(x))\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直方图主要用于研究数据得频率分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 100    # mean of distribution\n",
    "sigma = 20  # standard deviation of distribution\n",
    "x = mu + sigma * np.random.randn(2000)\n",
    "\n",
    "plt.hist(x, bins = 20, color = 'red', density = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Matplotlib也可以胜任比较复杂得定制化图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\mathtext.py:849: MathTextWarning: Font 'rm' does not have a glyph for '\\u4e2d' [U+4e2d]\n",
      "  MathTextWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\mathtext.py:850: MathTextWarning: Substituting with a dummy symbol.\n",
      "  warn(\"Substituting with a dummy symbol.\", MathTextWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\mathtext.py:849: MathTextWarning: Font 'rm' does not have a glyph for '\\u6587' [U+6587]\n",
      "  MathTextWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Polygon\n",
    "def func(x):\n",
    "    return -(x - 1) * (x - 6) + 50\n",
    "\n",
    "x = np.linspace(0, 10)\n",
    "y = func(x)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(x, y, 'r', linewidth = 2)\n",
    "plt.ylim(ymin = 0)\n",
    "\n",
    "a, b = 2, 9\n",
    "ix = np.linspace(a, b)\n",
    "iy = func(ix)\n",
    "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
    "poly = Polygon(verts, facecolor = '0.9', edgecolor = '0.5')\n",
    "ax.add_patch(poly)\n",
    "\n",
    "plt.text(0.5 * (a + b), 20, r\"$\\int_a^b (-(x - 1) * (x - 6) + 50)\\mathrm{d}x$\", horizontalalignment = 'center', fontsize = 20)\n",
    "\n",
    "plt.figtext(0.9, 0.05, '$x$')\n",
    "plt.figtext(0.1, 0.9, '$y$')\n",
    "\n",
    "ax.set_xticks((a, b))\n",
    "ax.set_xticklabels(('$a$', '$b$'))\n",
    "ax.set_yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试中文字符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'测试')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c/ALGz1rJ0x8vp1b1yvz+B724/OSWhM7ulvJOwS8i/yVjwx5GTUpj0aa9XNyjOfdf2o3GdavHuyyJIgW/iAChpmpP/2s542etpFHtaoz70SkM7N4s3mVJKVDwiwjfrNrJmMlprNy+n/9JbcXYwV2pX6tqvMuSUqLgF0lg+w7m8fj7S/jr7DW0aliTv93QlzM6JMW7LCllCn6RBDVz6VbGTkln094cruufwm8u7ERtNVVLCPpXFkkwu/Yf4qHpi5jy7w20b1KHST/rxyltGsa7LIkhBb9IgnB33kvfzH3TMtidncv/ntueW85tT/UqaqqWaBT8Iglgy94c7nk7g48WbaFHy/pMvL4vXVvUi3dZEicKfpEKzN15a+46Hn53MYfyCrhzUGduOKMtVdRULaEp+EUqqLU7srlzahpfZu6gT9tGPHZ5D9qpqZoQ2RW4WgMTgWZAATDe3Z8uNOdqYHTw7T7gZndfECxbDWQB+UBepP2iReTY5Bc4r3y1mt99uJTKlYyHL+vOVX2S1VRN/iOSLf484Nfu/p2Z1QXmmdkMd18UNmcVcLa77zKzQYSunds3bPkAd98evbJFpCjLt2QxanIa/167mwGdGvPbYT1o0aBmvMuSMuaowe/um4BNwf0sM1sMtAQWhc35KuwhswldVF1EYuRQXgHjPlvBnz9ZTp3qVfjjD09i6Ekt1FRNilSiffxmlgL0BuYcYdoNwPth3zvwkZk58Ly7jy9hjSJyBAvW7Wb05DSWbM5iSK8W3DekK0l11FRNihdx8JtZHWAycLu77y1mzgBCwX9G2HB/d99oZk2AGWa2xN1nFfHYkcBIgOTk5BL8CCKJ6cChfP748TJe+HwljetW54Ufp3JB16bxLkvKgYiC38yqEgr919x9SjFzegIvAoPcfcfhcXffGHzdamZTgT7A94I/+CQwHkIXWy/hzyGSUGav3MGYyWms3pHNlX1aM2ZQF+rXVFM1iUwkZ/UY8BKw2N3/UMycZGAKcI27Lwsbrw1UCo4N1AYuBB6MSuUiCSgrJ5fH3l/Ca3PWktyoFq/f2Jd+7dVUTUomki3+/sA1QLqZzQ/G7gKSAdx9HHAvcALwXHAw6fBpm02BqcFYFeB1d/8gqj+BSIL4ZMkWxk7NYMveHG48oy2/urAjtarpT3Gk5CI5q+cL4IinBrj7jcCNRYyvBHodc3Uiwo59B3lw+iLemb+Rjk3r8NzV/eidrKZqcuy0uSBSRrk7/0zbxP3TFpKVk8tt53XglgHtqVZF7Rbk+Cj4RcqgzXtyuPvtdD5evJVererz+BV96dxMTdUkOhT8ImWIu/Pmt+t45N3F5BYUMHZwF64/oy2V1W5BokjBL1JGrN6+nzunpPP1yh2c1q4Rj13ek5Sk2vEuSyogBb9InOUXOBO+WMXvZyylaqVKPHp5D0ac2lrtFqTUKPhF4mjp5ixGTVrAgvV7OL9LEx6+rAfN6teId1lSwSn4ReLgUF4Bz87M5LlPM6lboyp/urI3Q3o211a+xISCXyTG5q/bzahJC1i2ZR9DT2rBfUO60ah2tXiXJQlEwS8SI9mH8vjDR8uY8OUqmtStwUvXpnJeFzVVk9hT8IvEwFeZ2xkzJZ21O7O5um8yowd1pl4NNVWT+FDwi5SiPQdyefS9xbz57TpSTqjFmyNP47R2J8S7LElwCn6RUjJj0RbufjudbVkHuemsdtx+fkdqVqsc77JEFPwi0bZ930Hun7aQ6Wmb6NysLi/8OJWerRrEuyyR/1Dwi0SJu/PO/I088M+F7DuYx68u6MjPzj5RTdWkzFHwi0TBxt0HGDs1nZlLt3FS6wY8cUVPOjatG++yRIqk4Bc5DgUFzmvfrOXx95eQX+Dcc0lXftIvRU3VpEw76mdQM2ttZjPNbLGZLTSz24qYY2b2JzPLNLM0Mzs5bNm1ZrY8uF0b7R9AJF5Wbd/PiBdmc8/bGfRqXZ8Pbz+LG9RJU8qBSLb484Bfu/t3ZlYXmGdmM9x9UdicQUCH4NYX+AvQ18waAfcBqYAHj53m7rui+lOIxFBefgEvfrGKp2Yso1qVSjwxvCc/SG2ldgtSbkRy6cVNwKbgfpaZLQZaAuHBPxSY6O4OzDazBmbWHDgHmOHuOwHMbAYwEHgjqj+FSIws2riX0ZPTSN+whwu6NuXhy7rTtJ6aqkn5UqJ9/GaWAvQG5hRa1BJYF/b9+mCsuHGRcuVgXj7PfJLJXz5dQYNaVXn2qpMZ3KOZtvKlXIo4+M2sDjAZuN3d9xZeXMRD/AjjRT3/SGAkQHJycqRliZS6eWt2MXpyGplb9zGsd0vuvaQrDdVUTcqxiILfzKoSCv3X3H1KEVPWA63Dvm8FbAzGzyk0/mlRr+Hu44HxAKmpqUX+chCJpf0H8/jdR0t55avVNK9Xg5evO5UBnZrEuyyR43bU4LfQZ9mXgMXu/odipk0DbjWzNwkd3N3j7pvM7EPgETNrGMy7ELgzCnWLlKrPl2/jzinprN91gB+f3oZRAztTp7rOfpaKIZJ3cn/gGiDdzOYHY3cByQDuPg54DxgMZALZwHXBsp1m9hDwbfC4Bw8f6BUpi/Zk5/Lb9xbx1tz1tE2qzVs3nU6fto3iXZZIVEVyVs8XFL2vPnyOA7cUs2wCMOGYqhOJoQ8yNnPPOxns3H+Im885kdvO60CNqmqqJhWPPrtKwtuWFWqq9m76Jro0r8eEa0+lR6v68S5LpNQo+CVhuTtTvtvAg9MXceBQPndc1ImRZ7WjamU1VZOKTcEvCWn9rmzumprBrGXbOKVNQx4f3pP2TerEuyyRmFDwS0IpKHD+NmcNj7+/BAfuH9KVH5+eQiX115EEouCXhLFi2z7GTE7j29W7OLNDEo8M60HrRrXiXZZIzCn4pcLLzS/ghc9X8sePl1OjSiWevKInV5yipmqSuBT8UqFlbNjD6MlpLNy4l4HdmvHgZd1oUldN1SSxKfilQsrJzedP/1rO87NW0rBWNf5y9ckM6tE83mWJlAkKfqlw5q7eyajJaazctp8rTmnF3Rd3oUEtNVUTOUzBLxXGvoN5PPnBEibOXkOL+jWZeH0fzurYON5liZQ5Cn6pED5bto27pqSzcc8Brj09hTsu6kRtNVUTKZL+Z0i5tjv7EA9NX8zk79bTrnFt/nHT6aSmqKmayJEo+KXcei99E/e+k8Gu7FxuGXAivzhXTdVEIqHgl3Jn694c7n1nIR8s3Ey3FvV49fo+dGuhpmoikVLwS7nh7vxj3noenr6InLwCRg/szE/PbEsVNVUTKREFv5QL63Zmc9fUdD5fvp1TUxry2PCenNhYTdVEjkUkl16cAFwCbHX37kUsvwO4Ouz5ugCNg6tvrQaygHwgz91To1W4JIb8Amfi16t58sOlGPDQ0G5c3beNmqqJHIdItvhfAZ4BJha10N2fBJ4EMLMhwC8LXV5xgLtvP846JQFlbs1i1KQ0vlu7m7M7Nua3w7rTqqGaqokcr0guvTjLzFIifL4rgTeOpyCR3PwCnv9sBX/6Vya1qlfmD//Ti2G9W6qpmkiURG0fv5nVAgYCt4YNO/CRmTnwvLuPj9brScWUvn4Pd0xawJLNWVzcszn3D+lG47rV412WSIUSzYO7Q4AvC+3m6e/uG82sCTDDzJa4+6yiHmxmI4GRAMnJyVEsS8qDnNx8/vjxcl74fCWNalfj+WtO4aJuzeJdlkiFFM3gH0Gh3TzuvjH4utXMpgJ9gCKDP/g0MB4gNTXVo1iXlHFzVu5gzJR0Vm3fzw9TW3PX4C7Ur1U13mWJVFhRCX4zqw+cDfwobKw2UMnds4L7FwIPRuP1pGLIysnliQ+W8tfZa2jVsCZ/u6EvZ3RIindZIhVeJKdzvgGcAySZ2XrgPqAqgLuPC6YNAz5y9/1hD20KTA0OyFUBXnf3D6JXupRnM5dsZezUdDbtzeH6/m35zUUdqVVNf1YiEguRnNVzZQRzXiF02mf42Eqg17EWJhXTzv2HeGj6Iqb+ewMdmtRh0s/6cUqbhvEuSyShaBNLYsLdeTd9E/e9s5A9B3L533Pbc8u57aleRU3VRGJNwS+lbsveHO5+O4MZi7bQo2V9/nZjX7o0rxfvskQSloJfSo2789bcdTz87mIO5RVw56DO3HCGmqqJxJuCX0rF2h3ZjJmSxlcrdtCnbSMeH96Ttkm1412WiKDglyjLL3Be/nIVv/9oGZUrGQ9f1p2r+iSrqZpIGaLgl6hZtiXUVG3+ut2c27kJD1/WnRYNasa7LBEpRMEvx+1QXgF/+XQFz8xcTp3qVXh6xElc2quFmqqJlFEKfjkuC9btZvTkNJZszmJIrxbcP6QrJ9RRUzWRskzBL8fkwKF8nvp4GS9+vpLGdavzwo9TuaBr03iXJSIRUPBLiX29Ygd3Tklj9Y5sruzTmjsHd6FeDTVVEykvFPwSsb05uTz2/hJen7OW5Ea1eP3GvvRrr6ZqIuWNgl8i8q/FWxg7NYOtWTn89My2/OqCTtSspnYLIuWRgl+OaMe+gzzwz0VMW7CRTk3rMu6aUzipdYN4lyUix0HBL0Vyd6Yt2MgD/1xEVk4ut5/fgZ+f055qVdRuQaS8U/DL92zac4C7p2bwryVb6dW6AU8M70mnZnXjXZaIRImCX/6joMB589t1PPreYnILCrj74i5c178tldVuQaRCOerndjObYGZbzSyjmOXnmNkeM5sf3O4NWzbQzJaaWaaZjYlm4RJdq7fv56oXZ3PX1HS6t6zPh7efxY1ntlPoi1RAkWzxvwI8A0w8wpzP3f2S8AEzqww8C1wArAe+NbNp7r7oGGuVUpCXX8CEoKlatcqVeOzyHvzw1NZqtyBSgUVy6cVZZpZyDM/dB8gMLsGImb0JDAUU/GXEks17GT0pjQXr93B+lyY8fFkPmtWvEe+yRKSURWsf/+lmtgDYCPzG3RcCLYF1YXPWA32LewIzGwmMBEhOTo5SWVKUg3n5PDtzBc/NzKR+zar8+creXNKzubbyRRJENIL/O6CNu+8zs8HA20AHoKgU8eKexN3HA+MBUlNTi50nx+ffa3cxenIay7bs47KTWnDvkG40ql0t3mWJSAwdd/C7+96w+++Z2XNmlkRoC7912NRWhD4RSBxkH8rj9x8tY8KXq2hWrwYTfpLKuZ3VVE0kER138JtZM2CLu7uZ9SF0ptAOYDfQwczaAhuAEcBVx/t6UnJfZm5nzJQ01u08wNV9kxkzqDN11VRNJGEdNfjN7A3gHCDJzNYD9wFVAdx9HHAFcLOZ5QEHgBHu7kCemd0KfAhUBiYE+/4lRvYcyOXR9xbz5rfrSDmhFm+OPI3T2p0Q77JEJM4slNFlS2pqqs+dOzfeZZRrHy3czN1vZ7B930F+elY7fnl+R2pUVVM1kYrKzOa5e2okc/WXuxXM9n0HuX/aQqanbaJzs7q8eG0qPVupqZqI/D8FfwXh7rw9fwMP/HMR2Qfz+fUFHbnp7BPVVE1EvkfBXwFs3H2AsVPTmbl0G72TQ03VOjRVUzURKZqCvxwrKHBe+2Ytj723mAKHey/pyrX9UtRfR0SOSMFfTq3cto8xk9P5ZvVOzmifxKOX96B1o1rxLktEygEFfzmTl1/Ai1+s4qkZy6hWpRJPDO/JD1Jbqd2CiERMwV+OLNq4l1GTF5CxYS8Xdm3KQ5d1p2k9NVUTkZJR8JcDB/PyeeaTTP7y6Qoa1KrKs1edzOAezbSVLyLHRMFfxs1bs5PRk9PJ3LqPy09uyT0Xd6WhmqqJyHFQ8JdR+w/m8eSHS3n169W0qF+TV647lXM6NYl3WSJSASj4y6DPl2/jzinprN91gB+f3oZRAztTp7r+qUQkOpQmZcie7FwefncR/5i3nnZJtXnrptPp07ZRvMsSkQpGwV9GfJCxmXveyWDn/kPcfM6J3HZeBzVVE5FSoeCPs61ZOdw/bSHvpW+ma/N6vPyTU+nesn68yxKRCkzBHyfuzuTvNvDQ9EUcyM3njos6MfKsdlStrKZqIlK6IrkQywTgEmCru3cvYvnVwOjg233Aze6+IFi2GsgC8oG8SHtFV3Trd2Vz19QMZi3bxiltGvL48J60b1In3mWJSIKIZIv/FeAZYGIxy1cBZ7v7LjMbROiC6X3Dlg97/1hNAAAK5klEQVRw9+3HVWUFUVDg/HX2Gh7/YAkAD1zajWtOa0MlNVUTkRg6avC7+ywzSznC8q/Cvp1N6KLqUsiKbfsYPSmNuWt2cWaHJB4ZpqZqIhIf0d7HfwPwftj3DnxkZg487+7jo/x6ZV5ufgHjZ63k6X8tp2bVyvzuB70YfnJLtVsQkbiJWvCb2QBCwX9G2HB/d99oZk2AGWa2xN1nFfP4kcBIgOTk5GiVFVcZG/YwenIaCzfuZXCPZtx/aTea1FVTNRGJr6gEv5n1BF4EBrn7jsPj7r4x+LrVzKYCfYAigz/4NDAeQhdbj0Zd8ZKTm8+f/rWc52etpGGtaoz70ckM7N483mWJiABRCH4zSwamANe4+7Kw8dpAJXfPCu5fCDx4vK9X1n27eiejJ6Wxcvt+fnBKK+6+uCv1a1WNd1kiIv8RyemcbwDnAElmth64D6gK4O7jgHuBE4Dngv3Wh0/bbApMDcaqAK+7+wel8DOUCfsO5vHEB0uY+PUaWjaoycTr+3BWx8bxLktE5HsiOavnyqMsvxG4sYjxlUCvYy+t/Phs2TbumpLOxj0H+Em/FO64qBO11VRNRMoopdNx2J19iAenL2LKdxs4sXFt/nHT6aSmqKmaiJRtCv5j4O68n7GZe9/JYHd2LrcOaM+t57ZXUzURKRcU/CW0dW8O97yTwYcLt9C9ZT1evb4P3VqoqZqIlB8K/gi5O/+Yt56Hpy8iJ6+A0QM789Mz21JFTdVEpJxR8Edg3c5s7pySzheZ2+mT0ojHhvegXWM1VROR8knBfwT5Bc7Er1fzxAdLqWTw0NBuXN1XTdVEpHxT8Bcjc2sWoyal8d3a3ZzdsTGPXN6Dlg1qxrssEZHjpuAvJDe/gHGfruDPn2RSq3plnvphLy47SU3VRKTiUPCHSV+/hzsmLWDJ5iwu7tmcBy7tRlKd6vEuS0QkqhT8hJqqPfXxMl6YtZKkOtV5/ppTuKhbs3iXJSJSKhI++Oes3MGYKems2r6fH6a25q6Lu1C/ppqqiUjFlbDBn5WTy+MfLOFvs9fSulFNXruxL/3bJ8W7LBGRUpeQwT9zyVbGTk1n094cbjijLb++sCO1qiXkqhCRBJRQabdz/yEemr6Iqf/eQIcmdZh8cz9OTm4Y77JERGIqIYLf3Zmeton7py1kz4Fc/ve8Dtwy4ESqV1FTNRFJPBU++LfszWHs1Aw+XryFnq3q87cb+9Kleb14lyUiEjcRdRgzswlmttXMMopZbmb2JzPLNLM0Mzs5bNm1ZrY8uF0brcKPxt1585u1nP+Hz/h8+TbuGtyZKTf3U+iLSMKLdIv/FeAZYGIxywcBHYJbX+AvQF8za0ToUo2pgAPzzGyau+86nqKPZu2ObMZMSeOrFTvo27YRjw/vSUpS7dJ8SRGRciOi4Hf3WWaWcoQpQ4GJ7u7AbDNrYGbNCV2rd4a77wQwsxnAQOCN4ym6OPkFzstfruJ3Hy2lSqVK/HZYd648NVlN1UREwkRrH39LYF3Y9+uDseLGo25Pdi7XvvwN89ft5tzOTfjtsO40r6+maiIihUUr+IvapPYjjH//CcxGAiMBkpOTS1xAvZpVaHNCLa7rn8KlvVqoqZqISDGidfmo9UDrsO9bARuPMP497j7e3VPdPbVx48YlLsDMeHpEb4aqk6aIyBFFK/inAT8Ozu45Ddjj7puAD4ELzayhmTUELgzGREQkTiLa1WNmbxA6UJtkZusJnalTFcDdxwHvAYOBTCAbuC5YttPMHgK+DZ7qwcMHekVEJD4iPavnyqMsd+CWYpZNACaUvDQRESkN0drVIyIi5YSCX0QkwSj4RUQSjIJfRCTBKPhFRBKMhU7IKVvMbBuw5hgfngRsj2I50aK6SkZ1lYzqKpmKWFcbd4/or1/LZPAfDzOb6+6p8a6jMNVVMqqrZFRXySR6XdrVIyKSYBT8IiIJpiIG//h4F1AM1VUyqqtkVFfJJHRdFW4fv4iIHFlF3OIXEZEjKDfBb2YDzWxpcEH3MUUsr25mfw+Wzwm/VKSZ3RmMLzWzi2Jc16/MbFFwEfp/mVmbsGX5ZjY/uE2LcV0/MbNtYa9/Y9iya81seXC7NsZ1PRVW0zIz2x22rDTX1wQz22pmGcUsNzP7U1B3mpmdHLasNNfX0eq6Oqgnzcy+MrNeYctWm1l6sL7mxriuc8xsT9i/171hy474Hijluu4IqykjeE81CpaV5vpqbWYzzWyxmS00s9uKmBO795i7l/kbUBlYAbQDqgELgK6F5vwcGBfcHwH8PbjfNZhfHWgbPE/lGNY1AKgV3L/5cF3B9/viuL5+AjxTxGMbASuDrw2D+w1jVVeh+b8AJpT2+gqe+yzgZCCjmOWDgfcJXVXuNGBOaa+vCOvqd/j1gEGH6wq+Xw0kxWl9nQNMP973QLTrKjR3CPBJjNZXc+Dk4H5dYFkR/ydj9h4rL1v8fYBMd1/p7oeANwld4D3cUODV4P4k4Dwzs2D8TXc/6O6rCF0zoE+s6nL3me6eHXw7m9BVyEpbJOurOBcBM9x9p7vvAmYAA+NU15XAG1F67SNy91nAka4VMRSY6CGzgQZm1pzSXV9HrcvdvwpeF2L3/opkfRXneN6b0a4rlu+vTe7+XXA/C1jM968/HrP3WHkJ/kgu2v6fOe6eB+wBTojwsaVZV7gbCP1GP6yGmc01s9lmdlmUaipJXcODj5STzOzwJTLLxPoKdom1BT4JGy6t9RWJ4movzfVVUoXfXw58ZGbzLHRN61g73cwWmNn7ZtYtGCsT68vMahEKz8lhwzFZXxbaDd0bmFNoUczeY9G62Hppi+Si7cd9wfdjUJKLyf8ISAXODhtOdveNZtYO+MTM0t19RYzq+ifwhrsfNLOfEfq0dG6Ejy3Nug4bAUxy9/ywsdJaX5GIx/srYmY2gFDwnxE23D9YX02AGWa2JNgijoXvCLUQ2Gdmg4G3gQ6UkfVFaDfPl/7fVwQs9fVlZnUI/bK53d33Fl5cxENK5T1WXrb4I7lo+3/mmFkVoD6hj3wRX/C9lOrCzM4HxgKXuvvBw+PuvjH4uhL4lNBWQEzqcvcdYbW8AJwS6WNLs64wIyj0MbwU11ckiqu9NNdXRMysJ/AiMNTddxweD1tfW4GpRG8X51G5+1533xfcfw+oamZJlIH1FTjS+6tU1peZVSUU+q+5+5QipsTuPVYaBzKifSP0yWQloY/+hw8IdSs05xb+++DuW8H9bvz3wd2VRO/gbiR19SZ0MKtDofGGQPXgfhKwnCgd5IqwruZh94cBs/3/DyStCuprGNxvFKu6gnmdCB1os1isr7DXSKH4g5UX898H3r4p7fUVYV3JhI5b9Ss0XhuoG3b/K2BgDOtqdvjfj1CArg3WXUTvgdKqK1h+eKOwdqzWV/CzTwT+eIQ5MXuPRW1ll/aN0BHvZYRCdGww9iChrWiAGsA/gv8E3wDtwh47NnjcUmBQjOv6GNgCzA9u04LxfkB68MZPB26IcV2PAguD158JdA577PXBeswErotlXcH39wOPFXpcaa+vN4BNQC6hLawbgJ8BPwuWG/BsUHc6kBqj9XW0ul4EdoW9v+YG4+2CdbUg+HceG+O6bg17f80m7BdTUe+BWNUVzPkJoRM+wh9X2uvrDEK7Z9LC/q0Gx+s9pr/cFRFJMOVlH7+IiESJgl9EJMEo+EVEEoyCX0QkwSj4RUQSjIJfRCTBKPhFRBKMgl9EJMH8HxJsTyOrDvncAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.font_manager import FontProperties\n",
    "font = FontProperties(fname = r'C:\\windows\\fonts\\simsun.ttc', size = 12)\n",
    "plt.plot([1, 2, 3])\n",
    "plt.title(u\"测试\", fontproperties = font)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习库Seaborn的用法\n",
    "seaborn是一个很好用的统计图形库\n",
    "它基于matplotlib开发，能够很好地支持Pandas和Numpy的数据结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先生成最常用的线性回归图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 518.85x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set()\n",
    "\n",
    "# 加载数据\n",
    "iris = sns.load_dataset('iris')\n",
    "\n",
    "# 绘图\n",
    "g = sns.lmplot(x = \"sepal_length\", y = \"sepal_width\", hue = \"species\", truncate = True, height = 6, data = iris)\n",
    "\n",
    "# 更改x，y轴的标签\n",
    "g.set_axis_labels(\"Sepal length (mm)\", \"Sepal width (mm)\")\n",
    "\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "seaborn也可以与matplotlib无缝对接。\n",
    "将三个直方图绘制在一起，使用的是matplotlib的子图功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlQTgAAgKlUejnp27ev+vbtW9kvCwAAqgivyn7BjIyMyn5JAABQhRguJwcOHCh1u8VikY+Pj4KDg9W4ceNyBwMAAO7JcDmZNGmSLBaLXfsGBwdr9uzZGj16tOFgAADAPRkuJ8uXL9f8+fMVEBCgcePGqXnz5vL19dXp06e1adMm/fzzz5o1a5YkKSEhQc8++6yCgoI0YMAAh4cHAKA6yb2WodRjx10dw27BEa3kW6umw89rsdlsNiMHzJ07V0lJSYqLi5O/v/8N2/Ly8jR27Fi1adNGL774oqxWqx566CHl5+drw4YNkqSoqChJUmJiooPeAgAA1cP5A/9WwsMzXB3Dbve8t0y33dXZ4ec1PFtn9+7duu+++24pJpLk4+OjESNGaPv27b+e3MNDgwcP1o8//ljxpAAAwC0YLife3t5KT08vcXtqaqpuvhjj5VXpk4IAAEAVZbic9OjRQ6tWrdLXX399y7bvv/9ea9as0d133y3p1495tm7dqoiIiIonBQAAbsHwJY2nnnpKhw4d0sMPP6zWrVsrPDxcPj4+On36tH744Qc1aNBA8+bNk9VqVc+ePZWRkaG3337bGdkBAEA1ZLic1K9fXx9++KFWrVqlXbt2ad++fSooKFCTJk00depUPfLII6pVq5bS0tLUt29fDR8+vOhKCgAAQFnKNRgkICBA06dP1/Tp00vcp06dOnr55ZfLHQwAALinco9Uzc3NVXp6ugoLC4vdHhoaWu5QAADcrCDruq6fTXZ1DLsFNAqTl3+Aq2NUSYbLSXp6up5//nnt2LGjxGIiSUlJSRUKBgDA710/m6z/+++qc0W+7ax5CmrVxtUxqiTD5eTll19WQkKCevbsqTZt2sjHx8cZuQAAgJsyXE52796tMWPGaNGiRc7IAwAA3Jzh+5wUFBSoffv2zsgCAABgvJzcdddd+uabb5yRBQAAwPjHOvPnz9eDDz6oxYsXa8iQIQoODpaHx60dh9k6AACgPAyXk+joaFmtVr377rt67733StyP2ToAAKA8DJeTqVOnymKxOCMLAKACrHm5yk9PcXUMu3nXDpGHj6+rY8CEDJeTmTNnOiMHAKCC8tNTdGXvJlfHsFvdPvfJt35jV8eACZVZTs6dO6fg4GD5+fkVLduDMScAAKA8yiwn/fv31+LFixUdHS1J6tevn10f6zDmBAAAlEeZ5WT69OmKiIi4YZkxJwAAwFnKLCczZsy4YZkxJwAAwJkM34QNAADAmQzP1omMjCzzYx0fHx/VrVtXHTp00PTp09WqVatyBwQAAO7FcDmZMWOGYmNjlZGRoe7du6t58+by9fXV6dOn9a9//UuSNGDAAGVkZOjzzz/X559/ro0bN94wbgUAXMFWWCBrXrarY9jNw6eGLJ6Gf00DVV65v+vj4+MVGRl5w7ozZ85o7NixatmypR599FFdvnxZEyZM0Ouvv65ly5ZVOCwAVIQ1L1t550+4OobdfG5rKc8aNV0dA6h0hsecvP/++5o0adItxUSSmjRpookTJ2r9+vWSpHr16mnMmDE6ePBgxZMCAAC3YLicZGZmyt/fv8Ttvr6+Sk9PL1oOCgpSTk5O+dIBAAC3Y7ictGvXThs2bLihgPwmIyNDGzduVJs2bYrWff311woLC6tYSgAA4DYMjzmZM2eOJk+erMGDBysmJkbh4eHy8fHRqVOn9PHHHys1NVX/+Z//KUmaMmWKvvjiCz377LMODw4AAKonw+WkY8eOWrt2rZYsWaK1a9fKarUWbevUqZNee+013XHHHbp8+bJOnjypRx99VBMmTHBoaAAAUH1ZbDabzcgBx48fV8uWLWWxWHTt2jWdOXNGBQUFatKkiYKDg8s8PioqSpKUmJhYvsQAUE7VfSqxNS9X+ekpTkzkWN61Q+Th42v3/gVZ13X9bLITEzlWQKMwefkHGDom91qGUo8dd1IixwuOaCXfWo6fUWa4nHTv3l0jR47Uk08+Wa4XLK2cZGTl6tiZK+U6rytENKmrmv72/2Bdz83X6ZRrTkzkWE1DainA19vu/XMLCnUls+oMfq4b6CdfL09DxxRarcrJL3RSIsfz8/aUpwc3ggZQtRj+WCcrK0uNGzd2RhYdO3NFjy79yCnndoa35gxTVESo3fufTrmmv276yomJHOv5+7qpbeO6du9/JTNHH39/2nmBHGxoh6YKrW3sr5qc/EL9fLnqFMzwerUU4Es5AVC1GP6tNXnyZL377rt8LAMAAJzC8JWTI0eOKCUlRZMmTZKfn59q164tj5suG1ssFu3cudNhIQEAgPswXE5yc3PVrl07Z2QBAAAwXk5iY2OdkQMAAEBSBR78V1BQoMOHD+v8+fPq0qWL/Pz8VFhYqKCgIEfmAwAAbqZc5SQhIUEvvviirlz5ddrvu+++q7y8PD3++OOaMWOGpkyZ4tCQgFn4eXsqvF4tV8ewm5+3sanSAGAGhsvJvn37NGfOHHXu3FlTpkzRyy+/LElq3LixWrduraVLlyokJEQxMTEODwu4mqeHB1NzAcDJDP+WXb58udq1a6c1a9bcUEBatGih9evXq1OnTlq9erVDQwIAAPdhuJwkJSVp6NCht0wfliQvLy8NGzZMp06dckg4AADgfgyXE29vbxUUFJS4PT09Xd7e9t/yHAAA4PcMl5MuXbpo06ZNys3NvWXbpUuXtH79et15550OCQcAANyP4QGxs2fP1gMPPKDhw4erV69eslgs2rVrl/bu3avNmzcrLy9Ps2bNckZWAADgBgxfOWnRooXWrVun+vXrKzY2VjabTWvXrtXq1asVFhamVatWqU2bNs7ICgAA3EC57nMSERGh2NhYpaenKzk5WVarVY0aNVJISIij86EKqRvop6Edmro6ht3qBvq5OgIAoBiGy8lDDz2k4cOHa9CgQapdu7Zq167tjFyogny9PBVaO8DVMQAAVZzhj3WSk5M1f/58de/eXY8//rh27typ/Px8Z2QDAABuyPCVk927d+u7777TJ598os8++0yfffaZatWqpcGDB2vYsGHq2rWrM3ICAAA3Ua4xJx07dlTHjh31zDPPKDExUZ988om2b9+uTZs2qX79+ho6dKjmzp3r6KwAAMANVPghIVFRUXruuee0cuVK9e7dWxcvXtR7773niGwAAMANlevKyW9++OEHffrpp/r000915swZ+fv7a+TIkYqOjnZUPgAA4GYMl5OkpCQlJCQUFRIvLy/17NlTTzzxhPr16ydfX19n5AQAAG7CcDkZOXKkLBaLoqKiNGXKFA0ZMkS1atVyRrZqp2lILT1/XzdXx7Bb0xC+rgCAyme4nMyZM0fR0dFq2LChM/JUawG+3mrbuK6rYwAAYGqGy8nUqVOdkQMAAECSHeWkf//+mj9/vvr371+0XBaLxaKdO3dWPB0AAHA7ZZaT0NBQ+fv737AMAADgLGWWk9jY2FKXAQAAHMnuMSfZ2dn64IMP9K9//UtHjx5Venq6LBaLgoODFRkZqf79+ys6Olo+Pj7OzAsAAKo5u8rJgQMH9Oc//1lXrlyRj4+PwsLC1KhRIxUUFCg9PV179uzR7t27tWzZMi1dulSdO3d2dm4AAFBNWWw2m620HU6cOKHRo0crMDBQTz/9tIYMGXLL1ZHMzEx9+umn+u///m9lZmZq8+bNCg8PL/Z8UVFRkqTExMRbtmVk5erYmSvlfS+VLqJJXdX056ZzAAA4UpnlZO7cufrf//1fbdmyRQ0aNCj1ZJcuXVJMTIwGDBigRYsWFbtPaeUEAACgzAf/7d+/X6NHjy6zmEhS/fr1NWLECB06dMgh4QAAgPsps5xcuXKlxI9oitO8eXOdP3++QqEAAID7KnNAbH5+vmrUqGH3CX19fXX9+vUSt9esWdPucwEAAPdj+Pb1FbVnz57KfkkAAFCF2FVO0tPTde7cObtOmJaWVqFAAADAvZU5WycyMlIWi8XwiZOSksodCgAAuK8yr5yMHDmyMnIAAABIsuPKCQAAQGUqcyoxAABAZaKcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU6GcAAAAU/Eq74G7d+/W3r17de7cOc2ePVs1atTQV199pdGjR8vX19eRGQEAgBux2Gw2m5ED8vPzNWvWLO3du1ceHh6yWq169913lZaWptmzZ6tdu3Z65513FBQU5KzMAACgGjP8sc4bb7yhzz//XC+88IJ27dql37rNoEGDtGDBAh09elTLly93eFAAAOAeDJeTrVu3avTo0RozZswNH994eXlp0qRJuv/++7Vr164Sj+/bt6/69u1bvrQAAKDaM1xOLly4oHbt2pW4PSIiQikpKSVuz8jIUEZGhtGXBQAAbsJwOWnQoIFOnjxZ4vbvv/9eISEhFQoFAADcl+FyMmzYMMXFxenLL78sWmexWCRJ69at0+bNmzVkyBDHJQQAAG7F8GydvLw8TZs2Td98842Cg4OVmpqq8PBwpaenKz09Xe3bt9fq1avl7+9f7PFRUVGSpMTExIqnBwAA1Y7hciJJVqtVH374obZv364zZ86osLBQjRo1Ur9+/TRmzBj5+PiUeCzlBFWZNSdHBSkXXR3Dbl4hDeTh5+fqGABgiOFycv78ed12223lfkHKCaqyvDM/6+qmWFfHsFvQfZPk0yTc1TEAwBDDY0769eunSZMm6f3339fVq1edkQkAALgxw+XkT3/6k65cuaLnnntOPXr00J/+9CclJCQoNzfXGfkAAICbKdeYE0k6duyYPv74Y3366adKTk5WQECABg4cqOjoaN19991FM3huxsc6qMr4WAcAnK/cD/6LiIhQRESEZs+erSNHjighIUG7du3Sli1bVLduXe3bt8+ROQEAgJsw/LFOcXJycmS1WmWz2WSz2eTlVe7OAwAA3Fy5W8TBgweVkJCg7du3KyUlRTVr1tTgwYO1aNEi3XXXXY7MWG3YbPmyWjNdHcNuHh6Bsli8XR0DAOBmDJeTF198Udu3b9elS5fk4+Oj3r17a/jw4erVq1ep9zeBZLVmKi/v366OYTcfn87y9Kzj6hgAADdjuJysW7dOXbt21eOPP65BgwYpMDDQGbkAAICbMlxOPv/8cx7sBwAAnKbMcnLgwAG1aNFCwcHBkqTTp0/r9OnTZZ6YcScAAKA8yiwnkyZN0pIlSxQdHV20XNI9TCTJZrPJYrEoKSnJcSkBAIDbKLOc/O1vf9Mdd9xRtPzSSy+VWk4AAAAqosxyMnLkyBuWR40aVer+hYWFOnfuXMVSAQAAt2V4QGybNm20ZMkSDRs2rNjtmzdv1t/+9jcdPHiwwuEAAPbLL8zW1ezzro5ht6Aat8nbs4arY8CEyiwnFy9e1FdffVW0bLO8ccjWAAAROklEQVTZdODAARUUFNyyr9Vq1bZt2/jYBwBc4Gr2ee07scLVMezWo+VU1Qts7uoYMKEyy0lwcLDefPPNohk6FotFcXFxiouLK/GYSZMmOSwgAABwL2WWE29vb7377rv65ZdfZLPZNHnyZD366KPq3r37Lft6eHgoODhYzZvThAEAQPnYNeYkNDRUoaGhkn6dvRMVFaUmTZo4NRgAAHBPhp9KPHLkyBKLidVqVUZGhnbs2FHhYAAAwD0Znq2TmZmpl156Sdu3b1dWVpZsNlux+3ETNgAAUB6Gy8nixYsVHx+vO+64QwEBAfriiy8UHR2tK1eu6MCBA/L09NQrr7zijKwAnCwnv0CXrma5Oobd6gf5y8/b8K8xACZn+Kd67969GjhwoF5//XWlpaWpW7dumjRpkjp06KCkpCRNmDBBJ0+edEZWAE526WqW4vYddXUMuz3QI1Jh9Wq5OgYABzM85iQ1NbVopk6dOnXUoEEDff/995J+vUHbfffdp23btjk2JQAAcBuGy0lAQICsVmvRclhYmH788cei5datW3P7egAAUG6Gy0mHDh2UkJCgwsJCSVLLli2VmJhYNDD21KlT8vHxcWxKAADgNgyXkz/84Q86ePCgBg8erKtXr2rkyJE6efKk/vCHP2jhwoVas2aNunbt6oysAADADRguJ926ddPbb7+tZs2aqVatWurQoYMWLlyoQ4cOaePGjbr99ts1b948Z2QFAABuoFxz8Hr06KEePXoULY8dO1ajRo1STk6OatWqpby8PIcFBAAA7sVwOenfv7/mz5+v/v3737Dex8dHPj4++uijj7Ro0SJ98803DgsJAIA7KCwsVG5urqtj2M3X11eenp4OP2+Z5SQ1NVU//fRT0fLZs2d1+PBh1ap1670FrFarduzYwZUTAADKITc3V7/88ourY9itcePG8vf3d/h5yywnvr6+mjNnjlJSUiRJFotFb731lt56661i97fZbLr33nsdmxIAALiNMstJQECA3njjDf3444+y2WyaP3++7r//fnXq1OmWfT08PBQcHKxu3bo5JSwAAKj+7Bpz0rZtW7Vt21aSdO7cOQ0cOFARERFODQYAANyT4QGxM2bMcEYOAAAASXaUk5tn59w8S6c4FotFO3furHg6AADgdsosJ6GhoTeMxA0NDXVqIABwFmt+tgquVZ1nf3nVCpWHdw1XxwAqXZnlJDY2ttRlAKgqCq6d09Wvip9paEZB3R6VT90Wro4BVDrDt68HAABwpjKvnDz44IOGT2qxWLR69epyBQIAAO6tzHJSle5UBwAAqr4yy8nu3bsrIwcAAIAkJ405SU1NdcZpAQCAGzB8EzZJ+vDDD7V9+3ZlZWXJarUWrS8sLNT169d14sQJHTlyxGEhAQCA+zBcTlasWKF//OMf8vb2VmBgoNLS0tSwYUOlp6crOztbfn5+mjRpUrnC5BYU6HJWZrmOdYV6/oHy9SpXvwMAACUw/D9rfHy8IiMjFRsbq7S0NA0cOFBr1qxRaGio4uLitGjRInXs2LFcYS5nZWrrse/LdawrDI/ooEa1ars6BgAA1YrhMSdnz55VTEyMAgMD1aRJEwUFBSkxMVGenp4aP3687r33XqYRAwCAcjNcTry8vBQQEFC0HB4ermPHjhUtd+3aVadPn3ZIOAAA4H4Ml5MWLVro22+/LVpu1qzZDYNfr127pry8PMekAwAAbsdwORk1apTi4+P15JNPKisrS/369VNiYqKWLVumTz75RKtWrVJkZKQzsgIAADdgeEDsuHHjdOHCBa1bt05eXl4aNGiQhg4dqmXLlkmSAgMD9eSTTzo8KAAAcA/lmgf7xBNPaObMmfL6/9Noly5dqrFjx+rq1avq1KmT6tat69CQAADYCq2y5uS7OobdPPy8ZfHk+brlUe6bdHjddH+Pu+66q8JhAAAoiTUnX3mnrrg6ht18mtWVZ4Cvq2NUSYbLib1PKV6zZo3hMAAAAIbLSXFPKbZarUpLS1Nubq4aNWqkVq1aOSQcAABwP4bLSUlPKS4sLNSuXbv07LPP6pFHHqlwMAAA4J4cNlLH09NTgwYN0pgxY/TKK6846rQAAMDNOHwYcdOmTXX06FFHnxYAALgJh5aTvLw8bd26lanEAACg3Bw2WycvL0+nTp3StWvXNHPmzAoHQ9VTkF+ozMwcV8ewW2Cgn7y8PV0dAwBwE4fM1pF+HXPSvHlzDRs2TOPHj69wMFQ9mZk5+v5Qsqtj2K3DHWGqXSeg7B0BAJWq3LN1rl27poKCAtWpU0cWi+WGfZKTk1W3bt0bnl4MAABgD0PlZPPmzdq2bZsOHjxY9ORhT09PdejQQTExMRo1apS8vLz05z//WZGRkXrppZecEhoAAFRfdpWTy5cva+bMmfr222/l7e2t9u3bKyQkRB4eHkpJSdGRI0f07bffav369erZs6eOHTumv//9787ODgAAqqEyy0leXp6mT5+uY8eOafbs2Zo4caL8/f1v2CcnJ0cbNmzQq6++qh9//FHTpk3jLrEAAKBcLDabzVbaDv/85z/13HPP6c0331Tv3r1L3M9qtWrMmDH64Ycf9MQTT2jatGnF7hcVFSVJSkxMvGVbbkGBLmdlGsnvUvX8A+XrZf8nYzZbvqzWqvP+PDwCZbF4272/O8zWsebkqCDlopMSOZ5XSAN5+PnZvX9OfoEuXc1yYiLHqh/kLz9v+38GrfnZKrh2zomJHMurVqg8vGvYvX9+YbauZp93YiLHCqpxm7w97X9/7vBU4sLCQuXm5jopkeP5+vrK09Pxsx7LLCfjxo1T7dq19cYbb5R6on/+859auHChIiMjZbFYtGnTpmL369u3ryRpz5495YwMAACqszIr3cmTJ9WtW7cyT3Tq1ClFR0dr1KhROnPmTIn77dmzh2ICAABKVOb10MLCQvnZcVl47ty5kqS4uDgVFhZWPBkAAHBLZV45CQsL06FDh+w+4aFDhxQWFlahUAAAwH2VWU7uueceffTRRzp+/HiZJzt69Kg++ugj3XvvvQ4JBwAA3E+Z5WTs2LEKCQnRww8/rH379pW43969ezVlyhSFhoZq7NixDg0JAADcR5mzdSTp+PHjevTRR3X+/HmFh4erc+fOCgkJkSRdunRJ//73v5WcnKzGjRvr7bffVrNmzZweHAAAVE92lRNJysrK0ooVK/Txxx8rOfnGh7uFhYUpOjpaU6ZMUY0a9s9ZBwAAuJnd5eT3UlJSdOnSJdlsNjVo0KDoKgoAAEBFlaucAAAAOIux++oCAAA4GeUEAACYCuUEAACYiv2P86zCZs6cqe3bt+uFF17QAw884Oo4FTZp0iTt37//hnU1a9bU7bffrhkzZqhLly4uSuZ43333ndasWaPExESlpaWpQYMG6tmzpx599FE1aNDA1fHK5eavn4eHh/z9/dWyZUuNGTNGo0ePlsVicWHCiivue/T3evTooXfeeacSEznevHnztHnz5hK3v/fee7r77rsrMZHjHT9+XGvXrtWXX36pS5cuycfHR5GRkRo9erRiYmKq7PfpvHnzdPDgQe3YsaPY7f369VO3bt304osvVnIy53j99df1xhtv6IcffnB1FLtV+3KSmpqqPXv2qHXr1oqLi6sW5USS2rdvr2effVbSr88/SktLU1xcnB555BHFx8erVatWLk5YcatXr9bf//533X333XrqqacUEhKin376SStXrtT27du1du1aNW3a1NUxy+X3X7+CggKlp6drx44dWrBggY4ePVq0rSr7/Xu8Wc2aNSs5jXM0bNhQr732WrHbWrZsWclpHGvbtm1asGCBWrdurWnTpik8PFyZmZnauXOnnnnmGX377bd6/vnnXR0T1VS1Lyfbtm2Tr6+vnnzySU2bNk2HDx9W+/btXR2rwgIDA3XHHXfcsK5Hjx7q1q2b4uPj9fTTT7somWMcPHhQL7/8siZPnqx58+YVre/atav69++vmJgYLVy4UKtWrXJdyAoo7us3YMAAhYSEaMWKFRoyZIiioqJclM4xinuP1Y2Pj0+1fI8//fSTFixYoD59+ujVV1+Vp6dn0bZ+/frp9ttv16JFixQTE6POnTu7MCmqq2o/5iQ+Pl7du3dXz549Vb9+fcXFxbk6ktP4+vrKz8+vyl5q/b133nlHtWvX1hNPPHHLtgYNGmjevHnq1q2bCgoKXJDOeR577DH5+flV6+9TmN/KlSvl6emphQsX3lBMfjNu3DgNHDhQOTk5LkgHd1Ctr5wkJSXp6NGjmjVrljw8PBQTE6N169Zp3rx5CgwMdHW8CrHZbEX/MdtsNl29elVr1qxRdna2Ro8e7eJ0FWOz2bRv3z4NGDBAvr6+xe4zYsSISk5VOQIDA9WhQwcdPHjQ1VEq7Pffozfz9PSsFiVaUrHvsaq/v127duk//uM/FBwcXOx2T09PLVu2rJJTOV51++OmOqnW5eSDDz5Q3bp11bt3b0nSqFGjtGLFCm3dulXjx493cbqK+frrr9W2bdtb1j/11FNq0aKFCxI5TlpamnJzcxUaGurqKC5Rt25dfffdd66OUWElfY9K0ooVK9SrV69KTuR4ycnJxb7HhQsXaty4cS5IVHFXr17V1atXix3PdfN/5haLpdgrK1VBSV87mEO1LSd5eXnatm2bhgwZoqysLElSvXr11LZtW8XFxVX5ctKhQwc999xzkn79CzUtLU2ffvqplixZIh8fHz344IMuTlh+v/2yKywsdHESVMTvv0dvVl0eDtqwYcNiryA0atTIBWkcw2q1Frv+8OHDuu+++25Y16VLF8XGxlZGLIcr6WsnSX/84x8rOQ1uVm3Lye7du5Wenq6NGzdq48aNt2z/7rvv1LFjRxckc4yAgIBbBvb27t1bFy5c0GuvvaYJEyZU2b9ogoKCFBAQoHPnzpW4T2ZmpiRV+Y/ninPx4sUqO03694r7Hq1ufHx8qt17rFOnjvz9/W/5+WvZsqU2bdpUtPzCCy9UdjSHKu1r5+PjU8lpcLNqW07i4+PVtGnTW36ACgoK9Nhjj2njxo1VupyUpE2bNvryyy+VmppapR/I2KNHD33zzTfKzc0tdtzJqlWr9D//8z9KSEhQeHi4CxI6R0ZGhv7v//5PQ4cOdXUUuLF+/fpp7969ysrKkr+/vySpRo0aN/xnHhAQwNVNOE21nK1z6dIl7du3T0OHDlXXrl1v+Ne9e3f17dtXCQkJunbtmqujOtzhw4cVFBRU4kC2quLhhx9Wenp6sfeQOHfunNatW6cOHTpUq2IiSW+//bZyc3M1duxYV0eBG5s6dary8vL0l7/8Rfn5+bdsv3btmi5evOiCZHAX1fLKyYcffqjCwsIS//ocMWKEPvvsM23dulUTJ06s5HSOkZmZqUOHDhUt5+TkaNu2bdq/f7+eeOKJKvuRzm86deqk6dOna9myZTp58qRiYmJUu3ZtHT16VO+88448PDy0ZMkSV8cst99//X67id7OnTu1efNmTZ06tVpc1bv5e/T3LBZLtXiP1VVkZKQWL16s+fPna/To0RozZoxatWql3Nxc7d+/X5s2bVJ2drYmTJjg6qiopqplOdm8ebMiIyNLnLXSq1cvBQcHKy4ursqWk8OHD99wt9saNWqoWbNm+stf/lJtfmHMnDlTbdu21bp16/Tiiy/q2rVruu222zRkyBBNmzZN9evXd3XEcvv9189isahmzZpq166dli9frgEDBrg4nWPc/D36e56enlXqVtru6J577lG7du20bt06rV+/XhcuXJD062DmsWPHaty4cWrYsKGLU6K6sthsNpurQwAAAPymWo45AQAAVRflBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmMr/A2k0qoQpX7mcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style = \"white\", context = \"talk\")\n",
    "rs = np.random.RandomState(7)\n",
    "\n",
    "# 将图分为 3* 1 的子图\n",
    "f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize = (8, 6), sharex = True)\n",
    "\n",
    "# 生成数据\n",
    "x = np.array(list(\"ABCDEFGHI\"))\n",
    "y1 = np.arange(1, 10)\n",
    "\n",
    "# 绘制柱状图\n",
    "sns.barplot(x, y1, palette = \"BuGn_d\", ax = ax1)\n",
    "\n",
    "# 更改y标签\n",
    "ax1.set_ylabel(\"Sequential\")\n",
    "\n",
    "# 生成新数据，绘制第二个图\n",
    "y2 = y1 - 5\n",
    "sns.barplot(x, y2, palette = \"RdBu_r\", ax = ax2)\n",
    "ax2.set_ylabel(\"Diverging\")\n",
    "\n",
    "# 重新排列数据， 绘制第三个图\n",
    "y3 = rs.choice(y1, 9, replace = False)\n",
    "sns.barplot(x, y3, palette = \"Set3\", ax = ax3)\n",
    "ax3.set_ylabel(\"Qualitative\")\n",
    "\n",
    "# 移除边框\n",
    "sns.despine(bottom = True)\n",
    "\n",
    "# 将y轴的tick设置为空（美化图形）\n",
    "plt.setp(f.axes, yticks = [])\n",
    "\n",
    "# 设置三个图的上下间隔\n",
    "plt.tight_layout(h_pad = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "热力图也是一种常见的统计图，热力图可以使用不同的颜色以及颜色的深浅来表示不同的数值，这样就可以方便直观地观察截面数据了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25ed97dd080>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "# 加载数据\n",
    "flights_long = sns.load_dataset(\"flights\")\n",
    "flights = flights_long.pivot(\"month\", \"year\", \"passengers\")\n",
    "\n",
    "# 绘制热力图\n",
    "f, ax = plt.subplots(figsize = (9, 6))\n",
    "sns.heatmap(flights, annot = True, fmt = \"d\", linewidths = .5, ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用highcharts画动态图\n",
    "下面的例子中使用的url有些问题，对方服务器返回 403 Forbidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "HTTPError",
     "evalue": "HTTP Error 404: Not Found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-4613d115aade>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mdata_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"http://www.highcharts.com/samples/data/jso1np.php?filename=aapl-ohlcv.json&callback=?\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjsonp_loader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msub_d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mr'(\\/\\*.*\\*\\/)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mohlc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\highcharts\\highstock\\highstock_helper.py\u001b[0m in \u001b[0;36mjsonp_loader\u001b[1;34m(url, prefix_regex, suffix_regex, sub_d, sub_by)\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[0mhdr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'User-Agent'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.7 (KHTML, like Gecko) Chrome/16.0.912.77 Safari/535.7'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m     \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhdr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m     \u001b[0mpage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"utf-8\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m     \u001b[1;31m# replace all the redundant info with sub_by\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m             \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[1;34m(self, request, response)\u001b[0m\n\u001b[0;32m    639\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    640\u001b[0m             response = self.parent.error(\n\u001b[1;32m--> 641\u001b[1;33m                 'http', request, response, code, msg, hdrs)\n\u001b[0m\u001b[0;32m    642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36merror\u001b[1;34m(self, proto, *args)\u001b[0m\n\u001b[0;32m    561\u001b[0m             \u001b[0mhttp_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m         \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 563\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    564\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    565\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    502\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 503\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    504\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    505\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_error_302\u001b[1;34m(self, req, fp, code, msg, headers)\u001b[0m\n\u001b[0;32m    753\u001b[0m         \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    754\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 755\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    756\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    757\u001b[0m     \u001b[0mhttp_error_301\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhttp_error_303\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhttp_error_307\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhttp_error_302\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m             \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[1;34m(self, request, response)\u001b[0m\n\u001b[0;32m    639\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    640\u001b[0m             response = self.parent.error(\n\u001b[1;32m--> 641\u001b[1;33m                 'http', request, response, code, msg, hdrs)\n\u001b[0m\u001b[0;32m    642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    643\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36merror\u001b[1;34m(self, proto, *args)\u001b[0m\n\u001b[0;32m    567\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhttp_err\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    568\u001b[0m             \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'default'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'http_error_default'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 569\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m \u001b[1;31m# XXX probably also want an abstract factory that knows when it makes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    502\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 503\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    504\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    505\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_error_default\u001b[1;34m(self, req, fp, code, msg, hdrs)\u001b[0m\n\u001b[0;32m    647\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mHTTPDefaultErrorHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 649\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    651\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mHTTPRedirectHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHTTPError\u001b[0m: HTTP Error 404: Not Found"
     ]
    }
   ],
   "source": [
    "from highcharts import Highstock\n",
    "from highcharts.highstock.highstock_helper import jsonp_loader\n",
    "\n",
    "H = Highstock()\n",
    "\n",
    "data_url = \"http://www.highcharts.com/samples/data/jso1np.php?filename=aapl-ohlcv.json&callback=?\"\n",
    "data = jsonp_loader(data_url, sub_d = r'(\\/\\*.*\\*\\/)')\n",
    "\n",
    "ohlc = []\n",
    "volume = []\n",
    "groupingUnits = [\n",
    "    ['week', [1]],\n",
    "    ['month', [1, 2, 3, 4, 6]]\n",
    "]\n",
    "\n",
    "for i in range(len(data)):\n",
    "    ohlc.append(\n",
    "    [\n",
    "        data[i][0], # the date\n",
    "        data[i][1], # open\n",
    "        data[i][2], # hight\n",
    "        data[i][3], # low\n",
    "        data[i][4], # close\n",
    "    ])\n",
    "    volume.append(\n",
    "    [\n",
    "        data[i][0], # the date\n",
    "        data[i][5], # the volume\n",
    "    ])\n",
    "    \n",
    "options = {\n",
    "    'rangeSelector': {\n",
    "        'selected': 1\n",
    "    },\n",
    "    'title': {\n",
    "        'text': 'AAPL Historical'\n",
    "    },\n",
    "    'yAxis': [{\n",
    "        'labels': {\n",
    "            'align': 'right',\n",
    "            'x': -3\n",
    "        },\n",
    "        'title': {\n",
    "            'text': 'OHLC'\n",
    "        },\n",
    "        'height': '60%',\n",
    "        'lineWidth': 2\n",
    "    },{\n",
    "        'labels': {\n",
    "            'align': 'right',\n",
    "            'x': -3\n",
    "        },\n",
    "        'title': {\n",
    "            'text': 'Volume'\n",
    "        },\n",
    "        'top': '65%',\n",
    "        'height': '35%',\n",
    "        'offset': 0,\n",
    "        'lineWidth': 2\n",
    "    }],\n",
    "}\n",
    "\n",
    "H.add_data_set(ohlc, 'candlestick', 'AAPL', dataGrouping = {\n",
    "    'units': groupingUnits\n",
    "})\n",
    "\n",
    "H.add_data_set(volume, 'column', 'Volume', yAxis = 1, dataGrouping = {\n",
    "    'units', groupingUnits\n",
    "})\n",
    "\n",
    "H.set_dict_options(options)\n",
    "H.save_file('highcharts')\n",
    "H"
   ]
  }
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